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BackgroundMetacognition， the capacity to monitor and control one's cognitive processes， has been identified as a crucial component of effective decision-making and behavioral adaptation. Previous research has revealed cognitive deficits in patients with major depressive disorder （MDD）， while findings about metacognitive capacities in patients with MDD have been inconsistent across studies， and the exact relationship between metacognitive capacities and clinical symptoms in MDD patients remains uncertain.ObjectiveTo examine the metacognitive capacities of adolescent hospitalized patients with MDD and to explore its relationship with depressive and anxiety symptoms， thus providing an unprecedented insight into the prevention of MDD.MethodsA coherent 56 adolescent hospitalized patients with MDD in the Psychiatry Department at the Affiliated Hospital of Southwest Medical University from March 2022 to June 2023 and met the diagnostic criteria for depression as defined by the Diagnostic and Statistical Manual of Mental Disorders， fourth edition （DSM-IV） were enrolled as MDD group. At the same time， 62 healthy individuals matched with the age and sex of the MDD group residing in Luzhou were concurrently selected as control group. The metacognitive ability of the two groups was evaluated by perceptual decision-making task and confidence rating task， and the indicators included confidence deviation， reaction time of confidence evaluation and metacognitive efficiency. Additionally， the severity of depressive and anxiety symptoms was measured with Beck Depression Inventory （BDI） and Beck Anxiety Inventory （BAI）. Pearson correlation analysis was utilized to examine the relationship between metacognitive capacities and clinical symptoms.ResultsMDD group scored higher on BDI and BAI when compared with control group （t=-13.722， -9.674， P0.05）. In terms of metacognitive performance， MDD group reported a reduction in overall confidence， confidence in correct decisions， confidence in incorrect decisions and metacognitive efficiency compared with control group （t=3.044， 2.769， 2.836， 3.667， P0.05）. Furthermore， metacognitive efficiency in MDD patients exhibited negative correlation with both BDI and BAI scores （r=-0.269， -0.290， P

Abstract Objective Immune checkpoint inhibitor (ICI) therapy activates the immune system to recognize and eliminate cancer cells that have escaped surveillance. This study aimed to compare the treatment outcome of advanced and recurrent cervical cancer patients treated with first-line platinum and paclitaxel with or without ICI. Methods Data from 69 advanced and recurrent cervical cancer patients treated with first-line ICI plus platinum and paclitaxel (N = 33) or first-line platinum and paclitaxel (N = 36) were reviewed between March 2020 and January 2023 in this retrospective study. Patients chose treatment based on the actual disease condition, patient willingness, and medical advice. Additionally, objective response rate (ORR), disease control rate (DCR), progression-free survival (PFS), and overall survival (OS) were calculated, and adverse events were gained. Results There was no difference in baseline data between patients receiving the two different treatments (all P > 0.05). Complete response rate (18.2% vs. 8.3%; P = 0.294), ORR (48.5% vs. 30.6%; P = 0.127), and DCR (81.8% vs. 72.2%; P = 0.345) tended to ascend in patients treated with ICI plus platinum and paclitaxel compared to those treated with platinum and paclitaxel, although there was no statistical significance. In patients treated with ICI plus platinum and paclitaxel, the median PFS was 10.3 months and the median OS was not reached. Meanwhile, the median PFS and OS were 7.7 and 16.9 months in patients treated with platinum and paclitaxel. PFS (P = 0.036) and OS (P = 0.033) were increased in patients treated with ICI plus platinum and paclitaxel versus those treated with platinum and paclitaxel, which was verified by multivariate Cox regression analyses (both P 0.05). Conclusion First-line ICI plus platinum and paclitaxel yields better treatment responses, longer survival, and non-differential adverse events versus first-line platinum and paclitaxel in advanced and recurrent cervical cancer patients.

Abstract This paper proposes an adaptive saturation module to enhance the transient stability of grid‐following inverters after voltage‐dip inception and fault‐clearance moment. The equal‐area criterion reveals that a large acceleration area will be obtained due to the step change of current reference according to grid codes, which may lead to the loss of synchronism with the grid. It is found that the saturation module used with the phase‐locked loop can enhance the transient stability performance by reducing the acceleration area. To simultaneously adapt to the two cases (the inception and clearance of the voltage‐dip fault), an adaptive saturation module that can automatically adjust the clamping mode is proposed, which is the main contribution of the paper. The selection of the threshold value for the saturation module is also presented. A comprehensive comparison is made between the proposed solution and state‐of‐art solutions. Finally, the effectiveness of the proposed control strategy is confirmed by the experimental tests.

Opacification of intraocular lens(IOL)is a rare postoperative complication of cataract surgery. Its occurrence may be associated with various factors, including manufacturing processes, IOL material, patient factors, and surgical intervention. IOL opacification has been reported in all kinds of materials, and the morphological changes and pathological features of IOL opacification from different materials have their own characteristics, with varying clinical manifestations. With the development of related researches, the understanding of IOL opacification is becoming more and more comprehensive. This article reviews the latest research progress in the morphology, etiology, diagnosis and treatment of IOL opacification, with a view to providing guidance for current clinical work and potential directions for future scientific research.

Abstract Objectives To assess the efficacy of the Chinese herbal medication Shugan Hewei formula (SHF) combined with rabeprazole in patients with refractory gastroesophageal reflux disease (rGERD). Method A total of 264 participants were randomly assigned to the treatment group (n = 132) receiving SHF granules (20 mg) combined with rabeprazole (10 mg) and the control group (n = 132) receiving placebo SHF granules (20 mg) combined with rabeprazole (20 mg). Both groups undergo 8 weeks of treatment and 2 weeks of follow-up. Results The treatment group showed higher total clinical symptom efficacy and lower total symptom scores compared to the control group. The treatment group was superior to the control group in reducing rGERD major symptom scores, including heartburn, retrosternal pain, regurgitation and belching, and acid regurgitation. Additionally, treatment group (Z = 8.169, P

Abstract Background Osteoarthritis (OA) is the most prevalent type of arthritis and the main reason for progressive disability in middle-aged and older people. Studies of candidate genes may provide a novel insight and treatment strategy for knee osteoarthritis (KOA). The aim of this study was to investigate the relationship between KOA susceptibility and single-nucleotide polymorphism (SNP) of the ADAMTS-5 gene. Materials and methods The case group included 188 patients from Luoyang Orthopedic Hospital with clinically and radiographically diagnosed primary KOA, and the control group included 100 age-matched individuals without KOA. Fifteen ADAMTS-5 SNPs were assayed using MALDI-TOF MS. Allelic and haplotypic frequencies were compared between the groups. The relationship between genotype distribution and risk of KOA was analyzed by multivariate logistic regression. Results The frequency of A allele in rs2249350 site in the KOA group was significantly lower (odds ratio [OR]: 0.761; 95% confidence interval [95% CI]: 0.612–0.947; P = 0.016), while that of C allele was higher than that in the control group (OR: 1.176; 95% CI: 1.025–1.351; P = 0.016). AA genotype and gene model, especially recessive gene model at rs2249350 locus, negatively correlated with KOA risk after adjustment for sex, body mass index, age, and occupation (AA vs. CC: OR: 0.288; 95% CI: 0.124–0.669; P = 0.004; AA vs. CA + CC: OR: 0.348; 95% CI: 0.162–0.749; P = 0.007). Meanwhile, one protective haplotype, GA (rs229054, rs2249350) (OR: 0.763; 95% CI: 0.614–0.949; P = 0.017), and one high-risk haplotype, GC (rs229054, rs2249350) (OR: 1.259; 95% CI: 1.032–1.537; P = 0.019), were found in this study. Conclusion Despite a limited sample size, our study suggests that the rs2249350 polymorphism in the ADAMTS-5 gene is one of the genetic factors influencing the risk of KOA. The A allele and AA genotype of rs2249350 may protect from KOA, whereas C allele and CC genotype increase the risk of KOA. In addition, the GA haplotype (rs229054, rs2249350) might be associated with a decreased risk of KOA, whereas the GC haplotype (rs229054, rs2249350) may be a risk factor for KOA. Additional larger-sized studies in more ethnically diverse populations are needed to confirm these findings.

Abstract A universal coordinate system that can ensemble the huge number of cells and capture their heterogeneities is of vital importance for constructing large-scale cell atlases as references for molecular and cellular studies. Studies have shown that cells exhibit multifaceted heterogeneities in their transcriptomic features at multiple resolutions. This nature of complexity makes it hard to design a fixed coordinate system through a combination of known features. It is desirable to build a learnable universal coordinate model that can capture major heterogeneities and serve as a controlled generative model for data augmentation. We developed UniCoord, a specially-tuned joint-VAE model to represent single-cell transcriptomic data in a lower-dimensional latent space with high interpretability. Each latent dimension can represent either discrete or continuous feature, and either supervised by prior knowledge or unsupervised. The latent dimensions can be easily reconfigured to generate pseudo transcriptomic profiles with desired properties. UniCoord can also be used as a pre-trained model to analyze new data with unseen cell types and thus can serve as a feasible framework for cell annotation and comparison. UniCoord provides a prototype for a learnable universal coordinate framework to enable better analysis and generation of cells with highly orchestrated functions and heterogeneities.

Jatropha curcas L. (J. curcas), a shrub plant of the Euphorbiaceae family, has received enormous attention as a promising biofuel plant for the production of biodiesel and medical potential in ethnopharmacology. However, the tumor-promoter toxin phorbol esters present in J. curcas raise concerns for health and environmental risk as its large-scale cultivation limits the use of meal obtained after oil extraction for animal feed. Here, we determined the variation of phorbol ester profiles and contents in eight J. curcas tissues by high-performance liquid chromatography (HPLC) and found phorbol esters present in all parts of the plant except the seed shell. We showed tissue-specific patterns of accumulation of phorbol esters and associated terpenoids at the transcriptional level with high transcript levels in reproductive and young tissues. Genes involved in the same module of terpenoids biosynthesis were positively correlated. We further present diverse abiotic and biotic stresses that had different effects on the accumulation of transcripts in terpenoids shared and branched terpenoid pathways in plant seedlings. The fine-tuning of terpenoids biosynthesis may link with ecological functions in plants under extreme environments and defense against pathogens.

Fermented foods made through microbial growth and enzymatic conversions have been integral to human diets for at least 10,000 years. Recent interest in fermented foods has surged due to their functional properties and health benefits. Cruciferous vegetables of the genus Brassica, such as cabbage, broccoli, and cauliflower, are commonly used to produce fermented foods like sauerkraut, kimchi, pao cai, fermented turnips, and others. These foods are rich in lactic acid bacteria (LAB) and bioactive compounds, which contribute to their potential health-promoting properties. We examined 12 clinical trials investigating fermented foods of the genus Brassica. These studies, which mainly assessed the health benefits of kimchi or sauerkraut consumption, found that regular intake can alleviate symptoms of irritable bowel syndrome (IBS), aid weight loss, and enhance metabolic health. Seven observational studies also observed health benefits when consuming fermented foods of the genus Brassica. Six of the seven observational studies on kimchi intake linked kimchi intake to reduced obesity risk and other health benefits. An observational study linked sauerkraut and cabbage consumption to reduced breast cancer risk. Despite these findings, the exact roles of various microorganisms and bioactive compounds within these health effects require further investigation. This review underscores the potential of fermented cruciferous vegetables as functional foods, and advocates for more clinical trials and mechanistic studies to understand and optimize their health benefits.

Abstract Cellulose fiber-based textiles are ubiquitous in daily life for their processability, biodegradability, and outstanding flexibility. Integrating cellulose textiles with functional coating materials can unlock their potential functionalities to engage diverse applications. Metal-organic frameworks (MOFs) are ideal candidate materials for such integration, thanks to their unique merits, such as large specific surface area, tunable pore size, and species diversity. However, achieving scalable fabrication of MOFs-textiles with high mechanical durability remains challenging. Here, we report a facile and scalable strategy for direct MOF growth on cotton fibers grafted via the diazonium chemistry. The as-prepared ZIF-67-Cotton textile (ZIF-67-CT) exhibits excellent ultraviolet (UV) resistance and organic contamination degradation via the peroxymonosulfate activation. The ZIF-67-CT is also used to encapsulate essential oils such as carvacrol to enable antibacterial activity against E. coli and S. aureus. Additionally, by directly tethering a hydrophobic molecular layer onto the MOF-coated surface, superhydrophobic ZIF-67-CT is achieved with excellent self-cleaning, antifouling, and oil-water separation performances. More importantly, the reported strategy is generic and applicable to other MOFs and cellulose fiber-based materials, and various large-scale multi-functional MOFs-textiles can be successfully manufactured, resulting in vast applications in wastewater purification, fragrance industry, and outdoor gears.

Abstract Background Lung cancer, with high morbidity and mortality, is the commonest respiratory system neoplasm, which seriously endangers the life safety of patients. In this study, the effect of PRPS2 on cell progression was preliminarily investigated. Methods Immunohistochemical staining, western blot and reverse transcription‐quantitative polymerase chain reaction (RT‐qPCR) were performed to verify the expression level of PRPS2 in lung cancer. Lung cancer cell lines with stable downregulation of PRPS2 were constructed in A549 cells and NCIH460 cells. The function of PRPS2 silencing on the proliferation ability was verified by the EdU and cell colony formation experiment. Scratch and transwell tests were conducted to verify the role of PRPS2 silencing on the migratory and invasive ability of cells. The impact of PRPS2 silencing on cell apoptosis and cell cycle was verified by flow cytometry test. The effects of PRPS2 silencing on apoptosis‐associated proteins were assessed by western blot assay. The function of PRPS2 silencing on tumor growth in vivo was studied through xenograft tumor experiment. Results In comparison with normal tissues, PRPS2 was upregulated in lung cancer tissues. PRPS2 knockdown notably hindered the migratory ability, invasive ability and proliferation, but accelerated cell apoptosis. In vivo experiments confirmed that PRPS2 silencing blocked the growth of transplanted tumors. Conclusion In lung cancer, PRPS2 silencing suppressed the malignant progression, indicating that PRPS2 might be a novel biomarker for lung cancer treatment and diagnosis.

Summary: Generative design of promoters has enhanced the efficiency of de novo creation of functional sequences. Though several deep generative models have been employed in biological sequence generation, including variational autoencoder (VAE) or Wasserstein generative adversarial network (WGAN), these models might struggle with mode collapse and low sample diversity. In this study, we introduce the multinomial diffusion model (MDM) for promoter sequence design and propose a structured set of criteria for effectively comparing the performance of generative models. In silico experiments demonstrate that MDM outperforms existing generative AI approaches. MDM demonstrates superior performance in various computational evaluations, remains robust during the training process, and exhibits a strong ability in capturing weak signals. In addition, we experimentally validated that the majority of our model designed promoters have expression activities in vivo, indicating the practicality and potential of MDM for bioengineering.

Jun N-terminal kinase pathway-associated phosphatase (JKAP) regulates CD4+ T-cell differentiation and immunity, which are linked to mental disorders. This study aimed to explore the relationships between JKAP and T helper 17 (Th17)/regulatory T (Treg) ratio, as well as their associations with anxiety and depression in postpartum women. Serum JKAP were measured by enzyme-linked immunosorbent assay and blood Th17 and Treg cells were measured by flow cytometry in 250 postpartum women. Anxiety and depression were evaluated by the 6-item State-Trait Anxiety Inventory (STAI6) and Edinburgh Postnatal Depression Scale (EPDS). Anxiety and depression rates were 22.0 and 28.4%, respectively, among postpartum women. Notably, JKAP was negatively associated with the STAI6 (P=0.002) and EPDS scores (P

Arteriosclerotic cerebral small vessel disease (aCSVD) is a major cause of stroke and dementia. Although its underlying pathogenesis remains poorly understood, both inflammaging and gut microbiota dysbiosis have been hypothesized to play significant roles. This study investigated the role of gut microbiota in the pathogenesis of aCSVD through a comparative analysis of the gut microbiome and metabolome between CSVD patients and healthy controls. The results showed that patients with aCSVD exhibited a marked reduction in potentially beneficial bacterial species, such as Faecalibacterium prausnitzli and Roseburia intestinalis, alongside an increase in taxa from Bacteroides and Proteobacteria. Integrated metagenomic and metabolomic analyses revealed that alterations in microbial metabolic pathways, including LPS biosynthesis and phenylalanine-tyrosine metabolism, were associated with the status of aCSVD. Our findings indicated that microbial LPS biosynthesis and phenylalanine-tyrosine metabolism potentially influenced the symptoms and progression of aCSVD via pro-inflammatory effect and modulation of systemic neurotransmitters, respectively. These results imply that gut microbiota characteristics may serve as indicators for early detection of aCSVD and as potential gut-directed therapeutic intervention target.

Abstract Flexible electro‐optical dual‐mode sensor fibers with capability of the perceiving and converting mechanical stimuli into digital‐visual signals show good prospects in smart human‐machine interaction interfaces. However, heavy mass, low stretchability, and lack of non‐contact sensing function seriously impede their practical application in wearable electronics. To address these challenges, a stretchable and self‐powered mechanoluminescent triboelectric nanogenerator fiber (MLTENGF) based on lightweight carbon nanotube fiber is successfully constructed. Taking advantage of their mechanoluminescent‐triboelectric synergistic effect, the well‐designed MLTENGF delivers an excellent enhancement electrical signal of 200% and an evident optical signal whether on land or underwater. More encouragingly, the MLTENGF device possesses outstanding stability with almost unchanged sensitivity after stretching for 200%. Furthermore, an extraordinary non‐contact sensing capability with a detection distance of up to 35 cm is achieved for the MLTENGF. As application demonstrations, MLTENGFs can be used for home security monitoring, intelligent zither, traffic vehicle collision avoidance, and underwater communication. Thus, this work accelerates the development of wearable electro‐optical textile electronics for smart human‐machine interaction interfaces.

Abstract Van der Waals semiconductors exemplified by two-dimensional transition-metal dichalcogenides have promised next-generation atomically thin optoelectronics. Boosting their interaction with light is vital for practical applications, especially in the quantum regime where ultrastrong coupling is highly demanded but not yet realized. Here we report ultrastrong exciton-plasmon coupling at room temperature in tungsten disulfide (WS2) layers loaded with a random multi-singular plasmonic metasurface deposited on a flexible polymer substrate. Different from seeking perfect metals or high-quality resonators, we create a unique type of metasurface with a dense array of singularities that can support nanometre-sized plasmonic hotspots to which several WS2 excitons coherently interact. The associated normalized coupling strength is 0.12 for monolayer WS2 and can be up to 0.164 for quadrilayers, showcasing the ultrastrong exciton-plasmon coupling that is important for practical optoelectronic devices based on low-dimensional semiconductors.

Abstract Background Sufentanil-induced cough is common during the induction of anesthesia. The objective of this study was to determine whether pretreatment with a small dose of esketamine is effective in treating sufentanil-induced cough. Methods 220 patients were screened, and 200 patients who had scheduled elective surgery and were between 18 and 70 years old were randomly divided into two groups. Before sufentanil was administered, esketamine group (group K) was injected with 0.15 mg/kg esketamine at 5 s, and control group (group C) was administered with the same volume. Within 1 min after sufentanil(0.4ug/kg) injection during induction, cough incidence and severity were evaluated. After sufentanil was injected, we recorded its hemodynamic changes and side effects. Results In the esketamine group (group K) and control group (group C), there was an incidence of cough of 5 and 34%, respectively. The esketamine group (group K) had a significantly lower incidence and severity of cough compared to the control group (group C) immediately after sufentanil injection (P 0.05). Conclusion In our study, we found that sufentanil-induced cough was significantly reduced by pretreatment with 0.15 mg/kg esketamine, but with no significant changes in the hemodynamic status. Trial registration Chinese Clinical Trial Registry (ChiCTR2200063821, registered date: 17/09/2022), http://www.chictr.org.cn

Abstract Installing different chemical entities onto crystalline frameworks with well-defined spatial distributions represents a viable approach to achieve ordered and complex synthetic materials. Herein, a covalent organic framework (COF-305) is constructed from tetrakis(4-aminophenyl)methane and 2,3-dimethoxyterephthalaldehyde, which has the largest unit cell and asymmetric unit among known COFs. The ordered complexity of COF-305 is embodied by nine different stereoisomers of its constituents showing specific sequences on topologically equivalent sites, which can be attributed to its building blocks deviating from their intrinsically preferred simple packing geometries in their molecular crystals to adapt to the framework formation. The insight provided by COF-305 supplements the principle of covalent reticular design from the perspective of non-covalent interactions and opens opportunities for pursuing complex chemical sequences in molecular frameworks.

Abstract In this study, we investigated the roles of ROCK1 in regulating structural and functional features of caveolae located at the cell membrane of cardiomyocytes, adipocytes, and mouse embryonic fibroblasts (MEFs) as well as related physiopathological effects. Caveolae are small bulb‐shaped cell membrane invaginations, and their roles have been associated with disease conditions. One of the unique features of caveolae is that they are physically linked to the actin cytoskeleton that is well known to be regulated by RhoA/ROCKs pathway. In cardiomyocytes, we observed that ROCK1 deficiency is coincident with an increased caveolar density, clusters, and caveolar proteins including caveolin‐1 and ‐3. In the mouse cardiomyopathy model with transgenic overexpressing Gαq in myocardium, we demonstrated the reduced caveolar density at cell membrane and reduced caveolar protein contents. Interestingly, coexisting ROCK1 deficiency in cardiomyocytes can rescue these defects and preserve caveolar compartmentalization of β‐adrenergic signaling molecules including β1‐adrenergic receptor and type V/VI adenylyl cyclase. In cardiomyocytes and adipocytes, we detected that ROCK1 deficiency increased insulin signaling with increased insulin receptor activation in caveolae. In MEFs, we identified that ROCK1 deficiency increased caveolar and total levels of caveolin‐1 and cell membrane repair ability after mechanical or chemical disruptions. Together, these results demonstrate that ROCK1 can regulate caveolae plasticity and multiple functions including compartmentalization of signaling molecules and cell membrane repair following membrane disruption by mechanical force and oxidative damage. These findings provide possible molecular insights into the beneficial effects of ROCK1 deletion/inhibition in cardiomyocytes, adipocytes, and MEFs under certain diseased conditions.

Background This randomised controlled study was aimed at investigating the effects of WeChat-based cognitive behavioural stress management (WB-CBSM) on the mental health of patients with early-stage cervical cancer treated with surgical resection.Methods A total of 184 patients with early-stage cervical cancer were randomised to receive either WB-CBSM (n = 92) or normal care (NC) (n = 92) for 8 weeks.Results Compared with the NC group, the WB-CBSM group exhibited reduced Hospital Anxiety and Depression Scale (HADS)–anxiety scores at months (M)1 (t = 2.022, P = 0.045), M3 (t = 2.575, P = 0.011), and M6 (t = 2.709, P = 0.007); anxiety rates at M3 (χ2 = 4.834, P = 0.028) and M6 (χ2 = 5.182, P = 0.023); HADS–depression scores at M3 (t = 2.069, P = 0.040) and M6 (t = 2.449, P = 0.015); and depression rates at M6 (χ2 = 4.268, P = 0.039). Moreover, the WB-CBSM group showed increased Functional Assessment of Chronic Illness Therapy–Spiritual Well-Being scale scores at M1 (t = −2.001, P = 0.047), M3 (t = −2.281, P = 0.024), and M6 (t = −3.501, P = 0.001); the Quality of Life Questionnaire-Core 30 (QLQ-C30) global health score at M3 (t = −2.034, P = 0.043) and M6 (t = −2.426, P = 0.016); and QLQ-C30 function score at M3 (t = −2.009, P = 0.046). However, the groups exhibited decreased EuroQol-5 dimension scores at M3 (t = 2.056, P = 0.041) and M6 ((t = 2.242, P = 0.026) and QLQ-C30 symptom scores at M1 (t = 2.026, P = 0.044) and M3 (t = 2.210, P = 0.028).Conclusion WB-CBSM reduced anxiety and depression and improved the spiritual well-being and quality of life of patients with early-stage cervical cancer treated with surgical resection.

ABSTRACTLaser powder bed fusion (LPBF) of multi-scaled structures is a prominent direction pursued by innovative designs and engineering applications. However, understanding the impact of scale effect on the effectiveness of actual structures is a pressing challenge. This work investigates the influence of structure scale on surface morphology and mechanical property by fabricating tensile samples with gauge diameters ranging from 0.2 mm to 5.0 mm. For the hatching scanning strategy, the surface roughness drastically reduces by ∼51% as the diameter increases. This appears to be the coupling results of heat accumulation, molten pool morphology at the end of single-track, overlap between multi-tracks within a layer, and successive rotational buildup between layers. Moreover, decreasing the diameter substantially reduces the tensile strength. Fortunately, a hatching-contour scanning strategy is proposed to eliminate the scale effect of surface morphology and strength. These efforts provide essential guidance for the broader application of multi-scaled structures.

Abstract The evolution of charge structure involved in lightning discharge of a thunderstorm over the central Tibetan Plateau is investigated for the first time, based on the data from very high frequency interferometer, radar and sounding. During the developing‐mature stage, the TP thunderstorm exhibited a tripolar charge structure evolved from an initial inverted dipole. At the mature stage, a bottom‐heavy tripole charge structure is clearly presented, with a strong lower positive charge center (LPCC) at temperatures above −10°C, a middle negative charge region between −30°C and −15°C, and an upper positive charge region at T

As economically significant species, bivalves maintain intimate contact with external microorganisms during respiration and filter feeding, rendering them prime candidates for investigating microbial communities. Although numerous studies have been conducted on the microbial communities inhabiting the mantles, the composition and functions of the microbial community residing within the coloured mantles remain unclear. The pigments in the coloured areas may attract some microorganisms related to pigments. Thus, in vitro culture can be utilized to rapidly assess the fundamental characteristics of the microbial community present in the coloured mantles. Our results indicated that the microbial community in the coloured mantles of three bivalves (Crassostrea gigas, Mytilus edulis, and Chlamys farreri) responded differently to various media, resulting in pigmentation occurring on tyrosine medium. Further analyses by Metabarcoding revealed a relatively low abundance of melanin-producing strains on the surface of coloured mantles, which may be related to the requirement of melanin-producing strains for a specific nutritional environment. Then, we observed that there were significant differences in the composition of the microbial communities in coloured mantles of the three bivalves, with different bivalves possessing distinct biomarkers. These microorganisms affect the host by regulating the immune system and aiding digestion, nutrient absorption, and nitrification. Correlation analysis showed that multiple metabolites of the three bivalves were strongly correlated with bacterial biomarkers and may play an important role in maintaining the dynamic balance of surface microbial communities. In conclusion, this study discussed the multifaceted effects of microorganisms on the dynamic equilibrium of bivalves microbial communities, exploring the intricate connection between the host and the microbe, as well as the composition of microbial communities. This lays the foundation for comprehending the intricate symbiotic relationship between bivalves and their microbial counterparts and offers a theoretical foundation for the future development of microbial preparations intended for bivalves body surfaces.

An experimental platform to examine the effects of single-phase immersion preheating on lithium-ion battery performance at low temperatures was set up in this study. The performance of lithium-ion batteries at low temperatures can be improved through immersion preheating. After preheating from −15 °C to 15 °C, the battery capacity can recover to over 80 % of its rated capacity. The three influencing factors are inlet flow rate, fluid temperature, and cell spacings. The inlet temperature exerts the most influence on the preheating performance. The warming rate can reach 1.63 °C/min at a temperature of 50 °C. While the increase in flow rate has less influence on the heating rate, it has a greater impact on the temperature difference between cells. The maximum temperature difference between cells can still be kept below 4 °C. The impact of spacing on the heating rate and temperature difference is minimal. The temperature difference can be controlled within 6 °C. Finally, it's important to note that immersion preheating consumes significant amount of energy. The energy consumption of preheating at an ambient temperature of −25 °C exceeds 80 % of the rated capacity of the experimental battery pack.

In this study, we compared the performance of different structures of mini-channel cold plate (MCP) - phase change material (PCM) hybrid thermal management on discharge and thermal runaway. The heat transfer structures for them can be categorized into three types: (a) uncoupled, (b) semi-coupled, and (c) fully coupled. Among these, the semi-coupled design B exhibits the best performance. Specifically, in the high rate discharge condition, the structure can control the battery temperature at 44.37 °C at low coolant mass flow rate, and the temperature at 36.47 °C at high mass flow rate, while the temperature difference (TD) of a single cell does not exceed 2.01 °C. In a low-multiplier discharge zero-energy system, the pack temperature rise can be limited to no more than 10 °C in 35 °C environment. Under external short-circuit conditions, a lower coolant mass flow rate is sufficient to keep the pack temperature at 43.22 °C. Under internal short-circuit thermal runaway (TR), lower coolant mass flow rate can prolong the TR of the adjacent cell up to 521 s, and higher mass flow rate can completely inhibit the occurrence of TR. The objective of this study is to offer valuable references for the design and implementation of hybrid MCP-PCM cooling systems.

Abstract The development of soft electronics and soft fiber devices has significantly advanced flexible and wearable technology. However, they still face the risk of damage when exposed to sharp objects in real‐life applications. Taking inspiration from nature, self‐healable materials that can restore their physical properties after external damage offer a solution to this problem. Nevertheless, large‐scale production of self‐healable fibers is currently constrained. To address this limitation, this study leverages the thermal drawing technique to create elastic and stretchable self‐healable thermoplastic polyurethane (STPU) fibers, enabling cost‐effective mass production of such functional fibers. Furthermore, despite substantial research into the mechanisms of self‐healable materials, quantifying their healing speed and time poses a persistent challenge. Thus, transmission spectra are employed as a monitoring tool to observe the real‐time self‐healing process, facilitating an in‐depth investigation into the healing kinetics and efficiency. The versatility of the fabricated self‐healable fiber extends to its ability to be doped with a wide range of functional materials, including dye molecules and magnetic microparticles, which enables modular assembly to develop distributed strain sensors and soft actuators. These achievements highlight the potential applications of self‐healable fibers that seamlessly integrate with daily lives and open up new possibilities in various industries.

Color plays a pivotal role in product design, as it can evoke emotional responses from users. Understanding these emotional needs is crucial for effective brand image design. This paper introduces a novel approach, the Brand Image Design using Deep Multi-Scale Fusion Neural Network optimized with Cheetah Optimization Algorithm (BID-DMSFNN-COA), for classifying product color brand images as “Stylish” and “Natural”. By leveraging deep learning techniques and optimization algorithms, the proposed method aims to enhance brand image accuracy and address existing challenges in product color trend forecasting research. Initially, data are collected from the Mnist Data Set. The data are then supplied into the pre-processing section. In the pre-processing segment, it removes the noise and enhances the input image utilizing master slave adaptive notch filter. The Deep Multi-Scale Fusion Neural Network optimized with cheetah optimization algorithm effectively classifies the product colour brand image as “Stylish” and “Natural”. Implemented on the MATLAB platform, the BID-DMSFNN-COA technique achieves remarkable accuracy rates of 99 % for both “Natural” and “Stylish” classifications. In comparison, existing methods such as BID-GNN, BID-ANN, and BID-CNN yield lower accuracy rates ranging from 65 % to 85 % for “Stylish” and 65 %–70 % for “Natural” product color brand image design. The simulation outcomes reveal the superior performance of the BID-DMSFNN-COA technique across various metrics including accuracy, F-score, precision, recall, sensitivity, specificity, and ROC analysis. Notably, the proposed method consistently outperforms existing approaches, providing higher values across all evaluation criteria. These findings underscore the effectiveness of the BID-DMSFNN-COA technique in enhancing brand image design through accurate product color classification.

Abstract Background Spiraea L. is a genus comprising approximately 90 species that are distributed throughout the northern temperate regions. China is recognized as the center of species diversity for this genus, hosting more than 70 species, including 47 endemic species. While Spiraea is well-known for its ornamental value, its taxonomic and phylogenetic studies have been insufficient. Results In this study, we conducted sequencing and assembly of the plastid genomes (plastomes) of 34 Asiatic Spiraea accessions (representing 27 Asiatic Spiraea species) from China and neighboring regions. The Spiraea plastid genome exhibits typical quadripartite structures and encodes 113–114 genes, including 78–79 protein-coding genes (PCGs), 30 tRNA genes, and 4 rRNA genes. Linear regression analysis revealed a significant correlation between genome size and the length of the SC region. By the sliding windows method, we identified several hypervariable hotspots within the Spiraea plastome, all of which were localized in the SC regions. Our phylogenomic analysis successfully established a robust phylogenetic framework for Spiraea, but it did not support the current defined section boundaries. Additionally, we discovered that the genus underwent diversification after the Early Oligocene (~ 30 Ma), followed by a rapid speciation process during the Pliocene and Pleistocene periods. Conclusions The plastomes of Spiraea provided us invaluable insights into its phylogenetic relationships and evolutionary history. In conjunction with plastome data, further investigations utilizing other genomes, such as the nuclear genome, are urgently needed to enhance our understanding of the evolutionary history of this genus.

Abstract Profiling spatial variations of cellular composition and transcriptomic characteristics is important for understanding the physiology and pathology of tissues. Spatial transcriptomics (ST) data depict spatial gene expression but the currently dominating high-throughput technology is yet not at single-cell resolution. Single-cell RNA-sequencing (SC) data provide high-throughput transcriptomic information at the single-cell level but lack spatial information. Integrating these two types of data would be ideal for revealing transcriptomic landscapes at single-cell resolution. We develop the method STEM (SpaTially aware EMbedding) for this purpose. It uses deep transfer learning to encode both ST and SC data into a unified spatially aware embedding space, and then uses the embeddings to infer SC-ST mapping and predict pseudo-spatial adjacency between cells in SC data. Semi-simulation and real data experiments verify that the embeddings preserved spatial information and eliminated technical biases between SC and ST data. We apply STEM to human squamous cell carcinoma and hepatic lobule datasets to uncover the localization of rare cell types and reveal cell-type-specific gene expression variation along a spatial axis. STEM is powerful for mapping SC and ST data to build single-cell level spatial transcriptomic landscapes, and can provide mechanistic insights into the spatial heterogeneity and microenvironments of tissues.

BackgroundThe persistently high prevalence of insomnia seriously affects the quality of life of all populations. Studies showed that state-trait anxiety， resilience and neuroticism are related to the occurrence of insomnia， while the research on the relationship among the four factors in college students is still insufficient.ObjectiveTo discuss the impact of neuroticism on insomnia among college students， and to examine the mediating role of state-trait anxiety and resilience in the relationship between neuroticism and insomnia， thus providing references for the intervention of insomnia in college students.MethodsFrom September to December 2020， simple random sampling techniques were utilized to select 1 416 students in a university in Sichuan province， and all subjects were assessed using State-Trait Anxiety Inventory （STAI）， Connor-Davidson Resilience Scale （CD-RISC）， Chinese Big Five Personality Inventory-15 （CBF-PI-15） and Insomnia Severity Index （ISI）. Then Pearson correlation analysis was conducted to examine the correlation among the above four scales， and the mediating role of STAI and CD-RISC in the relationship between CBF-PI-15 neuroticism dimension and ISI was verified by Process macro mediation analysis.ResultsInsomnia was reported in 241/1 416 （17.02%） college students. The prevalence rate of insomnia in male students was higher than that in female students， with statistical difference （χ2=16.417， P

Abstract Background SARS-CoV-2, the pathogen of COVID-19, is a worldwide threat to human health and causes a long-term burden on the cardiovascular system. Individuals with pre-existing cardiovascular diseases are at higher risk for SARS-CoV-2 infection and tend to have a worse prognosis. However, the relevance and pathogenic mechanisms between COVID-19 and cardiovascular diseases are not yet completely comprehended. Methods Common differentially expressed genes (DEGs) were obtained in datasets of human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) infected with SARS-CoV-2 and myocardial tissues from heart failure patients. Further GO and KEGG pathway analysis, protein–protein interaction (PPI) network construction, hub genes identification, immune microenvironment analysis, and drug candidate predication were performed. Then, an isoproterenol-stimulated myocardial hypertrophy cell model and a transverse aortic constriction-induced mouse heart failure model were employed to validate the expression of hub genes. Results A total of 315 up-regulated and 78 down-regulated common DEGs were identified. Functional enrichment analysis revealed mitochondrial metabolic disorders and extensive immune inflammation as the most prominent shared features of COVID-19 and cardiovascular diseases. Then, hub DEGs, as well as hub immune-related and mitochondria-related DEGs, were screened. Additionally, nine potential therapeutic agents for COVID-19-related cardiovascular diseases were proposed. Furthermore, the expression patterns of most of the hub genes related to cardiovascular diseases in the validation dataset along with cellular and mouse myocardial damage models, were consistent with the findings of bioinformatics analysis. Conclusions The study unveiled the molecular networks and signaling pathways connecting COVID-19 and cardiovascular diseases, which may provide novel targets for intervention of COVID-19-related cardiovascular diseases.

Many Chinese migrant older adults are more prone to mental health problems due to their “migrant” status. During the COVID-19 pandemic, restrictions on their mobility exacerbated these conditions. Mental health is a crucial dimension of healthy aging. Network analysis offers a novel method for exploring interactions between mental health problems at the symptom level. This study employs network analysis to examine the interactions between comorbid depressive and anxiety symptoms across different stages of the COVID-19 pandemic. Surveys were conducted from September 2019 to January 2020 (T1), September 2020 to January 2021 (T2), and September 2021 onwards (T3). Depression and anxiety symptoms were measured by the Patient Health Questionnaire-9 (PHQ-9) and the Hospital Anxiety and Depression Scale-Anxiety (HADS-A). Expected Influence (EI) and Bridge Expected Influence (Bridge EI) were used to identify central and bridge symptoms in the network. Network stability and accuracy tests were performed. Among the Chinese migrant older adults, the anxiety prevalence was 18.50% at T1, 21.11% at T2, and 9.38% at T3. The prevalence of depression was 26.95% at T1, 55.44% at T2, and 60.24% at T3. The primary central symptoms included ‘Afraid something will happen’ (A2), ‘Irritability’ (A6), ‘Panic’ (A7), ‘Feeling of worthlessness’ (D6), ‘Anhedonia’ (D1), and ‘Feeling of fear’ (A5). The major bridge symptoms included ‘Feeling of fear’ (A5), ‘Panic’ (A7), ‘Irritability’ (A6), ‘Fatigue’ (D4), ‘Anhedonia’ (D1), and ‘Depressed or sad mood’ (D2). Differences in network structure were observed across the periods. The network analysis further revealed the evolving relationships between central and bridge symptoms over time, highlighting the importance of targeted intervention strategies for central and bridge symptoms of comorbid depression and anxiety at different periods.

Abstract Silicon (Si) is widely used as a lithium‐ion‐battery anode owing to its high capacity and abundant crustal reserves. However, large volume change upon cycling and poor conductivity of Si cause rapid capacity decay and poor fast‐charging capability limiting its commercial applications. Here, we propose a multilevel carbon architecture with vertical graphene sheets (VGSs) grown on surfaces of subnanoscopically and homogeneously dispersed Si–C composite nanospheres, which are subsequently embedded into a carbon matrix (C/VGSs@Si–C). Subnanoscopic C in the Si–C nanospheres, VGSs, and carbon matrix form a three‐dimensional conductive and robust network, which significantly improves the conductivity and suppresses the volume expansion of Si, thereby boosting charge transport and improving electrode stability. The VGSs with vast exposed edges considerably increase the contact area with the carbon matrix and supply directional transport channels through the entire material, which boosts charge transport. The carbon matrix encapsulates VGSs@Si–C to decrease the specific surface area and increase tap density, thus yielding high first Coulombic efficiency and electrode compaction density. Consequently, C/VGSs@Si–C delivers excellent Li‐ion storage performances under industrial electrode conditions. In particular, the full cells show high energy densities of 603.5 Wh kg−1 and 1685.5 Wh L−1 at 0.1 C and maintain 80.7% of the energy density at 3 C.

BackgroundFor nonmoyamoya patients with anterior cerebral artery (ACA) stenosis or occlusion, whether direct revascularization of the ACA territory can prevent stroke is still unclear. The objective of this study was to investigate the efficacy and safety of a parietal branch of superficial temporal artery-interposed superficial temporal artery-to-ACA bypass (PISAB) for preventing stroke in patients with symptomatic atherosclerotic ACA stenosis or occlusion (SAASO).MethodsWe retrospectively analyzed the data from patients with SAASO who had undergone PISAB in our center between April 2016 and November 2021. The rates of patency, satisfaction (revascularization grades A and B) of bypass, perioperative complications, recurrence of ischemic stroke, changes in bypass flow, and improvements in cerebral blood perfusion were analyzed.ResultsA total of 19 SAASO patients were involved in this study. Sixteen out of 19 (84.2%) patients were free from any cerebral ischemic events after surgery. Only 3 patients (15.8%) had recurrent stroke postoperatively. Two (10.5%) surgery-related complications occurred, including hyperperfusion syndrome and minor stroke. No skin ischemic complications occurred. The average follow-up period was 50.6 ± 18.3 months. The flow rate of the bypass was significantly increased half a year after surgery (56.2 ± 8.0 mL/min vs. 44.3 ± 5.3 mL/min, p

Summary: It is still a great challenge for the flexible piezoresistive pressure sensors to simultaneously achieve wide linearity and high sensitivity. Herein, we propose a high-performance textile pressure sensor based on chitosan (CTS)/MXene fiber. The hierarchical “point to line” architecture enables the pressure sensor with high sensitivity of 1.16 kPa−1 over an ultrawide linear range of 1.5 MPa. Furthermore, the CTS/MXene pressure sensor possesses a low fatigue over 1000 loading/unloading cycles under 1.5 MPa pressure load, attributed to the strong chemical bonding between CTS fiber and MXene and excellent mechanical stability. Besides, the proposed sensor shows good antibacterial effect benefiting from the strong interaction between polycationic structure of CTS/MXene and the predominantly anionic components of bacteria surface. The sensor is also applied to detect real-time human action, an overall classification accuracy of 98.61% based on deep neural network-convolutional neural network (CNN) for six human actions is realized.

Ambient ultraviolet radiation (UVB) from solar and artificial light presents serious environmental risks to aquatic ecosystems. The Pacific oyster, Crassostrea gigas, perceives changes in the external environment primarily through its mantle tissue, which contains many nerve fibers and tentacles. Changes within the mantles can typically illustrate the injury of ambient UVB. In this study, a comprehensive analysis of phenotypic, behavioral, and physiological changes demonstrated that extreme UVB radiation (10 W/m²) directly suppressed the behavioral activities of C. gigas. Conversely, under ambient UVB radiation (5 W/m²), various physiological processes exhibited significant alterations in C. gigas, despite the behavior remaining relatively unaffected. Using mathematical model analysis, the integrated analysis of the full-length transcriptome, proteome, and metabolome showed that ambient UVB significantly affected the metabolic processes (saccharide, lipid, and protein metabolism) and cellular biology processes (autophagy, apoptosis, oxidative stress) of the C. gigas mantle. Subsequently, using Procrustes analysis and Pearson correlation analysis, the association between multi-omics data and physiological changes, as well as their biomarkers, revealed the effect of UVB on three crucial biological processes: activation of autophagy signaling (key factors: Ca2+, LC3B, BECN1, caspase-7), response to oxidative stress (reactive oxygen species, heat shock 70, cytochrome c oxidase), and recalibration of energy metabolism (saccharide, succinic acid, translation initiation factor IF-2). These findings offer a fresh perspective on the integration of multi-data from non-model animals in ambient UVB risk assessment.

ABSTRACT: In the broiler-breeding industry, phenotype determination is critical. Leg weight is a fundamental indicator for breeding, and noninvasive testing technology can reduce damage to animals. This study proposes a broiler leg weight estimation system comprising a weight-estimation model and computed tomography (CT) acquisition equipment. The weight-estimation model can automatically process the scan results of live broiler chickens from the CT acquisition equipment. The weight-estimation model comprises an improved you-only-look-once (YOLOv5) segmentation algorithm and a random forest fitting network. The segmentation head was introduced into the YOLOv5 network, combined with a multiscale attention mechanism and an atrous spatial pyramid pooling architecture, and a new network model, YOLO- measuring chicken leg weight (YOLO-MCLW), was proposed to improve segmentation efficiency and accuracy. Morphological parameters were extracted from the obtained mask image, and a random forest network was used for fitting. The experiments show that the system exhibited an average absolute error of 7.27 g and an average percentage error of 4.82% in tests on 50 individual legs of 25 broiler chickens. The prediction R2 of broiler chicken legs can reaches 88.98%, the segmentation intersection over union result reaches 95.45%, and 37.04 images are processed per second. This system provides technical support for the part determination of broiler chickens in commercial breeding.

With a high percentage of distributed new energy sources connected to the power system, the power grid needs to reserve a larger margin to deal with the uncertainty of renewable energy outputs, leading to an increase in the cost of controlling the margins for the safe operation of the power grid. In order to reduce costs and increase efficiency, a quantitative assessment of new energy output uncertainty is needed. In this paper, a quantitative assessment method of new energy output uncertainty based on the prediction error is proposed, which makes use of a graph database to efficiently obtain massive new energy historical data, uses the clustering in quest (CLIQUE) algorithm to cluster the new energy historical data, and calculates the renewable energy real power confidence interval based on a given new energy power prediction, taking account of the impact of prediction errors caused by the new energy uncertainty and realizing the quantitative description of new energy output uncertainty. Finally, the method is calculated and analyzed together with the actual example data to verify the practical effect of the method.

Objective To investigate the correlations of the blood flow grade of lung adenocarcinoma on ultrasound with microvascular density (MVD) and expression of vascular endothelial growth factor (VEGF) and hypoxia-inducible factor-1α (HIF-1α) in tumor tissue as well as clinical stage and its significance. Methods A retrospective analysis was performed on the clinical data of 108 patients with lung adenocarcinoma. The patients were divided into blood flow level 0 (group A), blood flow level Ⅰ (group B), blood flow level Ⅱ (group C), and blood flow level Ⅲ (group D) based on the blood flow level of tumor tissue on ultrasound. The four groups were compared for MVD values and positive expression of VEGF and HIF-1α in the tumor tissue; meanwhile, analyses were performed for the correlations of the blood flow grade of lung adenocarcinoma on ultrasound with MVD and expression of VEGF and HIF-1 α in the tumor tissue as well as clinical stage. Results There were significant differences in MVD and positive expression rates of VEGF and HIF-1α in the tumor tissue between the groups (F=116.487,χ2=7.143,6.982,P

Abstract To achieve carbon neutrality, integrating intermittent renewable energy sources, such as solar and wind energy, necessitates the use of large-scale energy storage. Among various emerging energy storage technologies, redox flow batteries are particularly promising due to their good safety, scalability, and long cycle life. In order to meet the ever-growing market demand, it is essential to enhance the power density of battery stacks to lower the capital cost. One of the key components that impact the battery performance is the flow field, which is to distribute electrolytes onto electrodes. The design principle of flow fields is to maximize the distribution uniformity of electrolytes at a minimum pumping work. This review provides an overview of the progress and perspectives in flow field design and optimization, with an emphasis on the scale-up process. The methods used to evaluate the performance of flow fields, including both experimental and numerical techniques, are summarized, and the benefits of combining diverse methods are highlighted. The review then investigates the pattern design and structure optimization of serpentine- and interdigitated-based flow fields before discussing challenges and strategies for scaling up these flow fields. Finally, the remaining challenges and the prospects for designing highly efficient flow fields for battery stacks are outlined.

Background: In recent years, with the rapid development of mobile Internet and Web3D technologies, a large number of web-based online 3D visualization applications have emerged. Web3D applications, including Web3D online tourism, Web3D online architecture, Web3D online education environment, Web3D online medical care, and Web3D online shopping are examples of these applications that leverage 3D rendering on the web. These applications have pushed the boundaries of traditional web applications that use text, sound, image, video, and 2D animation as their main communication media, and resorted to 3D virtual scenes as the main interaction object, enabling a user experience that delivers a strong sense of immersion. This paper approached the emerging Web3D applications that generate stronger impacts on people's lives through “real-time rendering technology”, which is the core technology of Web3D. This paper discusses all the major 3D graphics APIs of Web3D and the well-known Web3D engines at home and abroad and classify the real-time rendering frameworks of Web3D applications into different categories. Results: Finally, this study analyzed the specific demand posed by different fields to Web3D applications by referring to the representative Web3D applications in each particular field. Conclusions: Our survey results show that Web3D applications based on real-time rendering have in-depth sectors of society and even family, which is a trend that has influence on every line of industry.

Abstract Background Cerebral microbleeds (CMBs) serve as neuroimaging biomarkers to assess risk of intracerebral hemorrhage and diagnose cerebral small vessel disease (CSVD). Therefore, detecting CMBs can evaluate the risk of intracerebral hemorrhage and use its presence to support CSVD classification, both are conducive to optimizing CSVD management. This study aimed to develop and test a deep learning (DL) model based on susceptibility-weighted MR sequence (SWS) to detect CMBs and classify CSVD to assist neurologists in optimizing CSVD management. Patients with arteriolosclerosis (aSVD), cerebral amyloid angiopathy (CAA), and cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) treated at three centers were enrolled between January 2017 and May 2022 in this retrospective study. The SWSs of patients from two centers were used as the development set, and the SWSs of patients from the remaining center were used as the external test set. The DL model contains a Mask R-CNN for detecting CMBs and a multi-instance learning (MIL) network for classifying CSVD. The metrics for model performance included intersection over union (IoU), Dice score, recall, confusion matrices, receiver operating characteristic curve (ROC) analysis, accuracy, precision, and F1-score. Results A total of 364 SWS were recruited, including 336 in the development set and 28 in the external test set. IoU for the model was 0.523 ± 0.319, Dice score 0.627 ± 0.296, and recall 0.706 ± 0.365 for CMBs detection in the external test set. For CSVD classification, the model achieved a weighted-average AUC of 0.908 (95% CI 0.895–0.921), accuracy of 0.819 (95% CI 0.768–0.870), weighted-average precision of 0.864 (95% CI 0.831–0.897), and weighted-average F1-score of 0.829 (95% CI 0.782–0.876) in the external set, outperforming the performance of the neurologist group. Conclusion The DL model based on SWS can detect CMBs and classify CSVD, thereby assisting neurologists in optimizing CSVD management.

Abstract Designing promoters with desirable properties is essential in synthetic biology. Human experts are skilled at identifying strong explicit patterns in small samples, while deep learning models excel at detecting implicit weak patterns in large datasets. Biologists have described the sequence patterns of promoters via transcription factor binding sites (TFBSs). However, the flanking sequences of cis-regulatory elements, have long been overlooked and often arbitrarily decided in promoter design. To address this limitation, we introduce DeepSEED, an AI-aided framework that efficiently designs synthetic promoters by combining expert knowledge with deep learning techniques. DeepSEED has demonstrated success in improving the properties of Escherichia coli constitutive, IPTG-inducible, and mammalian cell doxycycline (Dox)-inducible promoters. Furthermore, our results show that DeepSEED captures the implicit features in flanking sequences, such as k-mer frequencies and DNA shape features, which are crucial for determining promoter properties.

Abstract Patients with inflammatory bowel disease (IBD) always suffer from severe abdominal pain and appear to be at high risk for colorectal cancer. Recently, the co-delivery of targeted drugs and gut microbiota has developed into an attractive strategy. A new strategy using gut microbiota fermentation to overcome the interspace diffuse resistance from the mucus layer to control drug release in inflammatory bowel sites (IBS sites) has not yet been available. Here, we designed an alginate hydrogel microsphere encapsulating bifidobacterium (Bac) and drug-modified nanoscale dietary fibers (NDFs). The hydrogel microsphere is responsible for protecting drugs from acidic and multi-enzymatic environments and delivering drugs to the colorectum. Subsequently, the fermentation of Bac by digesting NDFs and proteins as carbon and nitrogen sources can promote drug release and play a probiotic role in the gut microbiota. In vitro evidence indicated that small-sized NDF (NDF-1) could significantly promote short-chain fatty acid (SCFA) expression. Notably, NDF-1 hydrogel microspheres showed a boost release of 5-ASA in the IBS sites, resulting in the amelioration of gut inflammation and remodeling of gut microbiota in chronic colitis mice. This study developed a controlled release system based on microbial fermentation for the treatment of IBD.

Massive computational resources are required by a booming number of artificial intelligence (AI) services in the communication network of the smart grid. To alleviate the computational pressure on data centers, edge computing first network (ECFN) can serve as an effective solution to realize distributed model training based on data parallelism for AI services in smart grid. Due to AI services with diversified types, an edge data center has a changing workload in different time periods. Selfish edge data centers from different edge suppliers are reluctant to share their computing resources without a rule for fair competition. AI services-oriented dynamic computational resource scheduling of edge data centers affects both the economic profit of AI service providers and computational resource utilization. This letter mainly discusses the partition and distribution of AI data based on distributed model training and dynamic computational resource scheduling problems among multiple edge data centers for AI services. To this end, a mixed integer linear programming (MILP) model and a Deep Reinforcement Learning (DRL)-based algorithm are proposed. Simulation results show that the proposed DRL-based algorithm outperforms the benchmark in terms of profit of AI service provider, backlog of distributed model training tasks, running time and multi-objective optimization.

Most biomolecular systems exhibit computation abilities, which are often achieved through complex networks such as signal transduction networks. Particularly, molecular competition in these networks can introduce crosstalk and serve as a hidden layer for cellular information processing. Despite the increasing evidence of competition contributing to efficient cellular computation, how this occurs and the extent of computational capacity it confers remain elusive. In this study, we introduced a mathematical model for molecular competition networks (MCNs) and employed a machine learning-based optimization method to explore their computational capacity. Our findings revealed that MCNs, when compared to their noncompetitive counterparts, demonstrate superior performance in both discrete decision-making and analog computation tasks. Furthermore, we highlighted the nonnegligible role of weak interactions and limited amounts of resources and examined how biological constraints influence the computational capacity of MCNs. The study suggested the potential of MCNs as efficient computational structures and provided new insights into cellular information processing.

The ultrasonic-assisted extraction process of polysaccharides from Stropharia rugosoannulata in Bo'ai County and the antibacterial and antioxidant activities were analyzed. On the basis of single factor experiments, with the polysaccharide yield as response value, the ultrasonic-assisted extraction process of polysaccharides from Stropharia rugosoannulata in Bo'ai County was optimized using Box-Behnken experimental design with three factors and three levels. The minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) of the polysaccharides extracted from Stropharia rugosoannulata in Bo'ai County were determined using the double dilution method and the plate coating method, respectively. The antioxidant activity was determined using ABTS method and FRAP methods. The results showed that the optimal ultrasonic-assisted extraction conditions were 62 ℃ (ultrasonic temperature), 1:30 g/mL (solid-liquid ratio) and 62 min (ultrasonic time). Under these conditions, the average yield of polysaccharide was 13.25%, which agreed well with the predicted value of 13.35%, the relative error was 0.75%. Among the five common pathogenic bacteria tested, the polysaccharide extract showed the strongest inhibitory effect on Salmonella typhimurium with the minimum inhibitory concentration of 6.25 mg/mL and the minimum bactericidal concentration of 12.5 mg/mL. Meanwhile, the polysaccharide extract showed strong ability to scavenge ABTS+ free radicals and reduce Fe3+, and showed dose-effect relationship within a certain concentration range, although weaker than the positive control (VC) at the same concentration. The results suggested that the regression models were stable and feasible for polysaccharide extraction from Stropharia rugosoannulata in Bo'ai County and the extract had strong antioxidant and antibacterial activities, but its antibacterial effect was selective.